The effects of environmental perturbation and measurement error on estimates of the shape parameter in the theta-logistic model of population regulation

The theta-logistic is a widely used generalisation of the logistic model of regulated biological processes which is used in particular to model population regulation. Then the parameter theta gives the shape of the relationship between per-capita population growth rate and population size. Estimation of theta from population counts is however subject to bias, particularly when there are measurement errors. Here we identify factors disposing towards accurate estimation of theta by simulation of populations regulated according to the theta-logistic model. Factors investigated were measurement error, environmental perturbation and length of time series. Large measurement errors bias estimates of theta towards zero. Where estimated theta is close to zero, the estimated annual return rate may help resolve whether this is due to bias. Environmental perturbations help yield unbiased estimates of theta. Where environmental perturbations are large, estimates of theta are likely to be reliable even when measurement errors are also large. By contrast where the environment is relatively constant, unbiased estimates of theta can only be obtained if populations are counted precisely Our results have practical conclusions for the design of long-term population surveys. Estimation of the precision of population counts would be valuable, and could be achieved in practice by repeating counts in at least some years. Increasing the length of time series beyond ten or 20 years yields only small benefits. if populations are measured with appropriate accuracy, given the level of environmental perturbation, unbiased estimates can be obtained from relatively short censuses. These conclusions are optimistic for estimation of theta. (C) 2008 Elsevier B.V All rights reserved.

Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated, representing a longitudinal structure. This model was proposed by Aoki et al. (2003b) and analyzed under the bayesian approach. In this article, considering the classical approach, we analyze asymptotic test statistics and present a simulation study to compare the behavior of the three test statistics for different sample sizes, parameter values and nominal levels of the test. Also, closed form expressions for the score function and the Fisher information matrix are presented. We consider two real numerical illustrations, the odontological data set from Hadgu and Koch (1999), and a quality control data set.

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.

Bayesian estimation of regression parameters in elliptical measurement error models

The main object of this paper is to discuss the Bayes estimation of the regression coefficients in the elliptically distributed simple regression model with measurement errors. The posterior distribution for the line parameters is obtained in a closed form, considering the following: the ratio of the error variances is known, informative prior distribution for the error variance, and non-informative prior distributions for the regression coefficients and for the incidental parameters. We proved that the posterior distribution of the regression coefficients has at most two real modes. Situations with a single mode are more likely than those with two modes, especially in large samples. The precision of the modal estimators is studied by deriving the Hessian matrix, which although complicated can be computed numerically. The posterior mean is estimated by using the Gibbs sampling algorithm and approximations by normal distributions. The results are applied to a real data set and connections with results in the literature are reported. (C) 2011 Elsevier B.V. All rights reserved.

Comparative analysis for reactive energy measurement methodologies, under non-sinusoidal conditions in three-phase four-wire circuits

This paper presents some methodologies for reactive energy measurement, considering three modern power theories that are suitable for three-phase four-wire non-sinusoidal and unbalanced circuits. The theories were applied in some profiles collected in electrical distribution systems which have real characteristics for voltages and currents measured by commercial reactive energy meters. The experimental results are presented in order to analyze the accuracy of the methodologies, considering the standard IEEE 1459-2010 as a reference. Finally, for additional comparisons, the theories will be confronted with the modern Yokogawa WT3000 energy meter and three samples of a commercial energy meter through an experimental setup. © 2011 IEEE.

Influence Assessment in an Heteroscedastic Errors-in-Variables Model

The main goal of this article is to consider influence assessment in models with error-prone observations and variances of the measurement errors changing across observations. The techniques enable to identify potential influential elements and also to quantify the effects of perturbations in these elements on some results of interest. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease.

Non-invasive phakometric measurement of corneal and crystalline lens alignment in human eyes

We describe a non-invasive phakometric method for determining corneal axis rotation relative to the visual axis (β) together with crystalline lens axis tilt (α) and decentration (d) relative to the corneal axis. This does not require corneal contact A-scan ultrasonography for the measurement of intraocular surface separations. Theoretical inherent errors of the method, evaluated by ray tracing through schematic eyes incorporating the full range of human ocular component variations, were found to be larger than the measurement errors (β < 0.67°, α < 0.72° and d < 0.08 mm) observed in nine human eyes with known ocular component dimensions. Intersubject variations (mean ± S.D.: β = 6.2 ± 3.4° temporal, α = 0.2 ± 1.8° temporal and d = 0.1 ± 0.1 mm temporal) and repeatability (1.96 × S.D. of difference between repeat readings: β ± 2.0°, α ± 1.8° and d ± 0.2 mm) were studied by measuring the left eyes of 45 subjects (aged 18-42 years, 29 females and 16 males, 15 Caucasians, 29 Indian Asians, one African, refractive error range -7.25 to +1.25 D mean spherical equivalent) on two occasions. © 2005 The College of Optometrists.

Effectively simultaneous temperature and strain measurement utilising a dual-grating sensor formed by type IA and type IIA FBGs

In this paper, we report a systematic investigation of the dependence of both temperature and strain sensitivities on the jiber Bragg grating (FBG) type, including the wellknown Type I, Type IIA, and a new type which we have designated Type 1.4, using both hydrogen-Ji-ee and hydrogenated B/Ge codoped jibers. We have identijed distinct sensitivity characteristics for each grating type, and we have utilised them to implement a novel dual-grating, duul-parameter sensor device. Three dual-grating sensing schemes with different combinations of gruting types have been constructed and compared. The Type IA-Type IIA combination exhibits the best pe$ormance and is superior to that of previously reported gruting-based structures. The characteristics of the measurement errors in such dualgrating sensor systems is also presented in detail.

Effectively simultaneous temperature and strain measurement utilising a dual-grating sensor formed by type IA and type IIA FBGs

In this paper, we report a systematic investigation of the dependence of both temperature and strain sensitivities on the jiber Bragg grating (FBG) type, including the wellknown Type I, Type IIA, and a new type which we have designated Type 1.4, using both hydrogen-Ji-ee and hydrogenated B/Ge codoped jibers. We have identijed distinct sensitivity characteristics for each grating type, and we have utilised them to implement a novel dual-grating, duul-parameter sensor device. Three dual-grating sensing schemes with different combinations of gruting types have been constructed and compared. The Type IA-Type IIA combination exhibits the best pe$ormance and is superior to that of previously reported gruting-based structures. The characteristics of the measurement errors in such dualgrating sensor systems is also presented in detail.

Proposição, validação e aplicação de um novo metodo para analise cinematica tridimensional da movimentação da caixa toracica durante a respiração

Universidade Estadual de Campinas . Faculdade de Educação Física

On uniquely determining cell-wall material properties with the compression experiment

A finite element model (FEM) of the cell-compression experiment has been developed in dimensionless form to extract the fundamental cell-wall-material properties (i.e. the constitutive equation and its parameters) from experiment force-displacement data. The FEM simulates the compression of a thin-walled, liquid-filled sphere between two flat surfaces. The cell-wall was taken to be permeable and the FEM therefore accounts for volume loss during compression. Previous models assume an impermeable wall and hence a conserved cell volume during compression. A parametric study was conducted for structural parameters representative of yeast. It was shown that the common approach of assuming reasonable values for unmeasured parameters (e.g. cell-wall thickness, initial radial stretch) can give rise to nonunique solutions for both the form and constants in the cell-wall constitutive relationship. Similarly, measurement errors can also lead to an incorrectly defined cell-wall constitutive relationship. Unique determination of the fundamental wall properties by cell compression requires accurate and precise measurement of a minimum set of parameters (initial cell radius, initial cell-wall thickness, and the volume loss during compression). In the absence of such measurements the derived constitutive relationship may be in considerable error, and should be evaluated against its ability to predict the outcome of other mechanical experiments. (C) 1998 Elsevier Science Ltd. All rights reserved.

Constructing good deals in discrete time arbitrage-free dynamic pricing models

Recent literature has proved that many classical pricing models (Black and Scholes, Heston, etc.) and risk measures (V aR, CV aR, etc.) may lead to “pathological meaningless situations”, since traders can build sequences of portfolios whose risk leveltends to −infinity and whose expected return tends to +infinity, i.e., (risk = −infinity, return = +infinity). Such a sequence of strategies may be called “good deal”. This paper focuses on the risk measures V aR and CV aR and analyzes this caveat in a discrete time complete pricing model. Under quite general conditions the explicit expression of a good deal is given, and its sensitivity with respect to some possible measurement errors is provided too. We point out that a critical property is the absence of short sales. In such a case we first construct a “shadow riskless asset” (SRA) without short sales and then the good deal is given by borrowing more and more money so as to invest in the SRA. It is also shown that the SRA is interested by itself, even if there are short selling restrictions.

Interlaminar fracture characterization of a carbon-epoxy composite in pure mode II

The interlaminar fracture toughness in pure mode II (GIIc) of a Carbon-Fibre Reinforced Plastic (CFRP) composite is characterized experimentally and numerically in this work, using the End-Notched Flexure (ENF) fracture characterization test. The value of GIIc was extracted by a new data reduction scheme avoiding the crack length measurement, named Compliance-Based Beam Method (CBBM). This method eliminates the crack measurement errors, which can be non-negligible, and reflect on the accuracy of the fracture energy calculations. Moreover, it accounts for the Fracture Process Zone (FPZ) effects. A numerical study using the Finite Element Method (FEM) and a triangular cohesive damage model, implemented within interface finite elements and based on the indirect use of Fracture Mechanics, was performed to evaluate the suitability of the CBBM to obtain GIIc. This was performed comparing the input values of GIIc in the numerical models with the ones resulting from the application of the CBBM to the numerical load-displacement (P-) curve. In this numerical study, the Compliance Calibration Method (CCM) was also used to extract GIIc, for comparison purposes.

Evaluation of Spatial interpolation techniques for mapping climate variables with low sample density: a case study using a new gridded dataset of Bangladesh

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Expectations, Learning and Monetary Policy: An Overview of Recent Research

Expectations about the future are central for determination of current macroeconomic outcomes and the formulation of monetary policy. Recent literature has explored ways for supplementing the benchmark of rational expectations with explicit models of expectations formation that rely on econometric learning. Some apparently natural policy rules turn out to imply expectational instability of private agents’ learning. We use the standard New Keynesian model to illustrate this problem and survey the key results about interest-rate rules that deliver both uniqueness and stability of equilibrium under econometric learning. We then consider some practical concerns such as measurement errors in private expectations, observability of variables and learning of structural parameters required for policy. We also discuss some recent applications including policy design under perpetual learning, estimated models with learning, recurrent hyperinflations, and macroeconomic policy to combat liquidity traps and deflation.